Making Computation Trustworthy
A scientific result produced by code is only as credible as the code that produced it. Reproducibility is the ability to re-run an analysis and obtain the identical result. It is a cornerstone of scientific integrity.
Good practice relies on these core elements:
| Practice | Purpose |
|---|---|
| Version control | Tracks changes, records why, and allows reverting to past states safely |
| Testing | Verifies code against known answers using unit tests |
| Documentation | Explains the why behind code for future reuse |
Code that runs without error is not automatically correct. A program can execute flawlessly yet still produce wrong answers from a logic bug.
Controlling Code and Randomness
Simulations introduce randomness, which threatens reproducibility. Setting a random seed fixes the pseudo-random sequence, making stochastic computations completely reproducible across runs.
You must also record the environment, including exact library versions and parameters, because results can shift with software updates.
| Pitfall | Consequence |
|---|---|
| Un-versioned scripts | Untraceable changes and lost history |
| No random seed | Non-reproducible stochastic outputs |
| Assuming error-free = correct | Hidden logic bugs or regressions slip through |
Good practice separates a throwaway script from a reliable scientific instrument.